Method and system for predicting the financial impact of environmental or geologic conditions

ABSTRACT

A system and method of predicting the financial impact of environmental or geologic events (that include one or more environmental or geologic conditions) by determining a recurrence interval of each past condition in each location, determining the correlation between the past condition and the observable financial impact of the past event, calculating a predicted observable financial impact of each past event, calculating a predicted financial impact of each past event recurring by multiplying the predicted observable financial impact of the past event by the recurrence interval of the past condition, grouping the past events into a plurality of groups based on the predicted financial impact of the past condition recurring, determining a threshold for each group, identifying current or forecasted conditions, and determining the predicted financial impact of the current or forecasted conditions by comparing the current or forecasted conditions with the thresholds.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/609,650, filed May 31, 2017, which claims priority to U.S.Provisional Patent Application No. 62/343,547, filed May 31, 2016, theentire contents of which are hereby incorporated by reference.

BACKGROUND

Predicting the impact of future weather, environmental, and/or geologicevents is vital to companies and governmental organizations. However,the impact of those events is not simply dependent on the magnitude ofthose events. While heavy winds in Seattle, Wash., for example, maycause significant damage and disruption, winds of that magnitude may nothave as much of an impact in Wichita, Kans., which has a history ofstrong winds (and an infrastructure and population that can withstandand adapt to those conditions). Accordingly, the impact of forecastedevents may be predicted as a function of the impact of similar events inthat location and the return frequency of those events in that location.

To date, the financial impact of future weather, environmental, andgeologic events has been performed by humans making subjectivedeterminations (e.g., meteorologists, environmental scientists,geologists, etc.). Those subjective determinations, however, have anumber of drawbacks. In addition to the increased time it takes for aperson (or a group of people) to make those subjective determinations,those subjective determinations are also inconsistent because they aredependent on the skill level and dispositions of the person (or people)making those determinations. As such, there is a need for a system thatuses specific mathematical rules to predict the financial impact offorecasted weather, environmental, and geologic events.

SUMMARY

In order to overcome the disadvantages in the prior art, there isprovided a system that uses specific mathematical rules to predict thefinancial impact environmental or geologic events. Accordingly, there isprovided a system and method for predicting the financial impact ofenvironmental or geologic events (that include one or moreenvironmental/geologic conditions) by determining a recurrence intervalof each past condition in each location, determining the correlationbetween the past condition and the observable financial impact of thepast event, calculating a predicted observable financial impact of eachpast event, calculating a predicted financial impact of each past eventrecurring by multiplying the predicted observable financial impact ofthe past event by the recurrence interval of the past condition,grouping the past events into a plurality of groups based on thepredicted financial impact of the past condition recurring, determininga threshold for each group, receiving current or forecasted conditions,and determining the predicted financial impact of the current orforecasted conditions by comparing the forecasted conditions with thethresholds.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of exemplary embodiments may be better understood with referenceto the accompanying drawings. The components in the drawings are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of exemplary embodiments, wherein:

FIG. 1 is a block diagram illustrating a peril index analytics systemaccording to an exemplary embodiment of the present invention;

FIG. 2 is a block diagram illustrating an overview of the architectureof the peril index analytics system according to an exemplary embodimentof the present invention;

FIG. 3 is a flow chart of a process for predicting the impact offorecasted weather conditions according to exemplary embodiments of thepresent invention;

FIG. 4 is a table of weather events in a location that have been rankedand grouped according to an exemplary embodiment of the presentinvention;

FIG. 5 is a table of weather events in another location that have beenranked and grouped according to an exemplary embodiment of the presentinvention;

FIGS. 6-7 illustrate the predicted impact of forecasted weatherconditions as determined and output in graphical format according toexemplary embodiments of the present invention;

FIG. 8A shows an example of forecasted weather conditions;

FIG. 8B illustrates the predicted impact of the forecasted weatherconditions shown in FIG. 8A according to an exemplary embodiment of thepresent invention;

FIG. 9A shows another example of forecasted weather conditions;

FIG. 9B illustrates the predicted impact of the forecasted weatherconditions shown in FIG. 9A according to an exemplary embodiment of thepresent invention;

FIG. 10A shows another example of forecasted weather conditions;

FIG. 10B illustrates the predicted impact of the forecasted weatherconditions shown in FIG. 10A according to an exemplary embodiment of thepresent invention;

FIG. 11A shows another example of forecasted weather conditions;

FIG. 11B illustrates the predicted impact of the forecasted weatherconditions shown in FIG. 11A according to an exemplary embodiment of thepresent invention;

FIGS. 12-14 illustrate the locations of alerts that may be output basedon forecasted weather conditions according to exemplary embodiments ofthe present invention;

FIG. 15 is a flow chart of a process for predicting the impact offorecasted environmental conditions; and

FIG. 16 is a flow chart of a process for predicting the impact offorecasted geologic conditions.

DETAILED DESCRIPTION

Reference to the drawings illustrating various views of exemplaryembodiments of the present invention is now made. In the drawings andthe description of the drawings herein, certain terminology is used forconvenience only and is not to be taken as limiting the embodiments ofthe present invention. Furthermore, in the drawings and the descriptionbelow, like numerals indicate like elements throughout.

FIG. 1 is a block diagram illustrating a peril index analytics system100 according to an exemplary embodiment of the present invention.

As shown in FIG. 1, the peril index analytics system 100 includes one ormore databases 110, an analysis unit 180, and a graphical user interface190. The one or more databases 110 include historical weather data 112,historical weather impact data 114, and forecasted weather conditions116. While most of the embodiments are described below with reference topredicting the effects of forecasted weather conditions, in someembodiments the peril index analytics system 100 may be used to predictthe effects of forecasted environmental conditions. Accordingly, the oneor more databases 110 may also include historical environmental data122, historical environmental impact data 124, and forecastedenvironmental conditions 126. In other embodiments the peril indexanalytics system 100 may be used to predict the effects of forecastedgeologic conditions. Accordingly, the one or more databases 110 may alsoinclude historical geologic data 132, historical geologic impact data134, and forecasted geologic conditions 136

The historical weather data 112 includes information indicative of thelocation, time, and severity of past weather events. Past weather eventsmay include, for example, hurricanes, tornadoes, thunderstorms, hail,floods, lightning, high winds, snow, floods, droughts, temperatureextremes, etc. Each weather event includes one or more weatherconditions (e.g., snow, rain, ice, wind, heat, cold, etc.). The severityof each past weather event may be measured in terms of the snowfallaccumulation, rainfall accumulation, ice accumulation, wind speed, hightemperature, low temperature, etc. The historical weather data 112 maybe received from publicly-available sources (e.g., the National Oceanicand Atmospheric Administration (NOAA) Storm Events Database), privatesources (e.g., AccuWeather, Inc., AccuWeather Enterprise Solutions,Inc.), etc.

The historical weather impact data 114 includes information indicativeof the damage and disruption associated with past weather events. Thedamage and disruption associated with past weather events may includedirect damage to property and crops as well as indirect disruptionattributable to the past weather events (e.g., power outages, lostsales, shipment delays data, reduced consumer spending, reduced visitsto retail and service locations, augmented traffic speeds, etc.). Thehistorical weather impact data 114 may be received frompublicly-available sources (e.g., the NOAA Storm Events Database, whichaggregates information from county, state and federal emergencymanagement officials, local law enforcement officials, skywarn spotters,National Weather Service (NWS) damage surveys, newspaper clippingservices, the insurance industry, the general public, etc.), informationfrom industry-specific commercial and non-commercial entities (e.g.,insurance claim information), third party sources, etc. The historicalweather impact data 114 may also include client-specific data (receivedfrom a client) that is used by the peril index analytics system 100 todetermine client-specific impacts of past weather events and predict theclient-specific impact of forecasted weather conditions 116.

The forecasted weather conditions 116 include information indicative ofthe predicted location, predicted time, and predicted magnitude offorecasted weather conditions (e.g., snow, rain, ice, wind, heat, cold,etc.) and events (e.g., hurricanes, tornadoes, thunderstorms, hail,lightning, high winds, snow, floods, droughts, temperature extremes,etc.) The forecasted weather conditions and events may be received fromAccuWeather, Inc., AccuWeather Enterprise Solutions, Inc., the NationalWeather Service (NWS), the National Hurricane Center (NHC), othergovernmental agencies (such as Environment Canada, the U.K. MeteorologicService, the Japan Meteorological Agency, etc.), private companies (suchas Vaisalia's U.S. National Lightning Detection Network, WeatherDecision Technologies, Inc.), individuals (such as members of theSpotter Network), etc.

The historical environmental data 122 includes information indicative ofthe location, time, and severity of past environmental events andenvironmental conditions (e.g., pollution, deforestation, depopulation,climate change, meteorological conditions, inhospitableness,biodegradable pollution, nonbiodegradable pollution, air pollution,noise pollution, sound pollution, thermal pollution, water pollution,etc.). The historical environmental data 122 may be received frompublicly-available sources (e.g., NOAA, the U.S. Geologic Survey, etc.),private sources, etc.

The historical environmental impact data 124 includes informationindicative of the damage and disruption associated with pastenvironmental events. The damage and disruption associated with pastenvironmental events may include direct damage to property and crops aswell as indirect disruption attributable to the past environmentalevents (e.g., power outages, lost sales, shipment delays data, reducedconsumer spending, reduced visits to retail and service locations,augmented traffic speeds, etc.). The historical environmental impactdata 124 may also include client-specific data (received from a client)that is used by the peril index analytics system 100 to determineclient-specific impacts of past environmental events and predictclient-specific impact of forecasted environmental conditions 126.

The forecasted environmental conditions 126 include informationindicative of the predicted location, predicted time, and predictedseverity of forecasted environmental events and forecasted environmentalcondition (e.g., pollution, deforestation, depopulation, climate change,meteorological conditions, inhospitableness, biodegradable pollution,nonbiodegradable pollution, air pollution, noise pollution, soundpollution, thermal pollution, water pollution, etc.). The forecastedenvironmental conditions may be received from AccuWeather, Inc.,AccuWeather Enterprise Solutions, Inc., the National Weather Service(NWS) the U.S. Geologic Survey, other governmental agencies, privatecompanies, etc.

The historical geologic data 132 includes information indicative of thelocation, time, and severity of past geologic events and geologicconditions (e.g., erosion, glaciation, volcanic eruption or emission,earthquakes, tsunamis, avalanches, landslides, mudslides, etc.). Thehistorical geologic data 132 may be received from publicly-availablesources (e.g., NOAA, the U.S. Geologic Survey, etc.), private sources,etc.

The historical geologic impact data 134 includes information indicativeof the damage and disruption associated with past geologic events. Thedamage and disruption associated with past geologic events may includedirect damage to property and crops as well as indirect disruptionattributable to the past geologic events (e.g., power outages, lostsales, shipment delays data, reduced consumer spending, reduced visitsto retail and service locations, augmented traffic speeds, etc.). Thehistorical geologic impact data 134 may also include client-specificdata (received from a client) that is used by the peril index analyticssystem 100 to determine client-specific impacts of past geologic eventsand predict client-specific impact of forecasted geologic conditions136.

The forecasted geologic conditions 136 include information indicative ofthe predicted location, predicted time, and predicted severity offorecasted geologic events and forecasted geologic condition (e.g.,erosion, glaciation, volcanic eruption or emission, earthquakes,tsunamis, avalanches, landslides, mudslides, etc.). The forecastedgeologic conditions may be received from AccuWeather, Inc., AccuWeatherEnterprise Solutions, Inc., the National Weather Service (NWS) the U.S.Geologic Survey, other governmental agencies, private companies, etc.

FIG. 2 is a drawing illustrating an overview of the architecture 200 ofthe peril index analytics system 100 according to an exemplaryembodiment of the present invention.

As shown in FIG. 2, the architecture 200 may include one or more servers210 and one or more storage devices 220 connected to a plurality ofremote computer systems 240, such as one or more personal systems 250and one or more mobile computer systems 260, via one or more networks230.

The one or more servers 210 may include an internal storage device 212and a processor 214. The one or more servers 210 may be any suitablecomputing device including, for example, an application server and a webserver which hosts websites accessible by the remote computer systems240. The one or more storage devices 220 may include external storagedevices and/or the internal storage device 212 of the one or moreservers 210. The one or more storage devices 220 may also include anynon-transitory computer-readable storage medium, such as an externalhard disk array or solid-state memory. The networks 230 may include anycombination of the internet, cellular networks, wide area networks(WAN), local area networks (LAN), etc. Communication via the networks230 may be realized by wired and/or wireless connections. A remotecomputer system 240 may be any suitable electronic device configured tosend and/or receive data via the networks 230. A remote computer system240 may be, for example, a network-connected computing device such as apersonal computer, a notebook computer, a smartphone, a personal digitalassistant (PDA), a tablet, a notebook computer, a portable weatherdetector, a global positioning satellite (GPS) receiver,network-connected vehicle, a wearable device, etc. A personal computersystem 250 may include an internal storage device 252, a processor 254,output devices 256 and input devices 258. The one or more mobilecomputer systems 260 may include an internal storage device 262, aprocessor 264, output devices 266 and input devices 268. An internalstorage device 212, 252, and/or 262 may include one or morenon-transitory computer-readable storage mediums, such as hard disks orsolid-state memory, for storing software instructions that, whenexecuted by a processor 214, 254, or 264, carry out relevant portions ofthe features described herein. A processor 214, 254, and/or 264 mayinclude a central processing unit (CPU), a graphics processing unit(GPU), etc. A processor 214, 254, and 264 may be realized as a singlesemiconductor chip or more than one chip. An output device 256 and/or266 may include a display, speakers, external ports, etc. A display maybe any suitable device configured to output visible light, such as aliquid crystal display (LCD), a light emitting polymer display (LPD), alight emitting diode display (LED), an organic light emitting diodedisplay (OLED), etc. The input devices 258 and/or 268 may includekeyboards, mice, trackballs, still or video cameras, touchpads, etc. Atouchpad may be overlaid or integrated with a display to form atouch-sensitive display or touchscreen.

Referring back to FIG. 1, the one or more databases 110 may be anyorganized collection of information, whether stored on a single tangibledevice or multiple tangible devices, and may be stored, for example, inthe one or more storage devices 220. The analysis unit 180 may berealized by software instructions stored on one or more of the internalstorage devices 212, 252, and/or 262 and executed by one or more of theprocessors 214, 254, or 264. The graphical user interface 190 may be anyinterface that allows a user to input information for transmittal to theperil index analytics system 100 and/or outputs information receivedfrom the peril index analytics system 100 to a user. The graphical userinterface 190 may be realized by software instructions stored on one ormore of the internal storage devices 212, 252, and/or 262 and executedby one or more of the processors 214, 254, or 264.

FIG. 3 is a flow chart of a process 300 for predicting the impact offorecasted weather conditions. The process 300 may be performed, forexample, by the analysis unit 180.

The observable impact of at least some of the past weather events isdetermined in step 302. As described above, the historical weatherimpact data 114 includes information indicative of the damage anddisruption associated with past weather events. In the simplestembodiment, the impact of a past weather event is the cost of physicaldamage (measured, for example, in dollars) caused by those weatherevents (as aggregated, for example, by the NOAA Storm Events Database).In other embodiments, the total observable impact of a past weatherevent is determined by adding the cost of physical damage caused thatweather event and the estimated cost (measured in dollars, man-hours,etc.) of indirect disruption associated with that weather event (e.g.,power outages, lost sales, lost productivity, reduced consumer spending,reduced visits to retail and service locations, augmented trafficspeeds, shipment delays, etc.) to determine an objective estimate of thetotal observable impact of that weather event. Finally, in otherembodiments, the peril index analytics system 100 may be used to predicta specific impact of forecasted weather events. In those embodiments,the impact of a weather event is the cost of that specific observableimpact.

The impact of individual weather events is determined by spatiallyjoining the damage and disruption information included in the historicalweather impact data 114 and the weather event information included inthe historical weather data 112 (e.g., merging the observable damage anddisruption information and the weather condition information by locationand date).

FIG. 4 is an exemplary table of weather events (in this example, eventsthat include snow) in a location (in this example, Alamosa, Colo.) withanalysis performed accordingly to an exemplary embodiment of the presentinvention. As shown in FIG. 4, some of the weather events include anobjective estimate of the observable impact (“IMPACT”) of the weatherevent.

FIG. 5 is an exemplary table of weather events (in this example, eventsthat include snow) in another location (in this example, Litchfield,Ill.) with analysis performed accordingly to an exemplary embodiment ofthe present invention. As shown in FIG. 4, some of the weather eventsinclude an objective estimate of the observable impact (“IMPACT”) of theweather event.

As described in detail below, each row includes information regarding aweather event, where:

-   -   “METAR” is the location as identified by the Meteorological        Terminal Aviation Routine Weather Report (METAR) station;    -   NMETAR is the numeric designation of the METAR station;    -   DATE is the date of the weather event;    -   SNOWFALL is the total daily snowfall in inches;    -   POPULATION is the estimated 2012 population;    -   ELEVATION is the elevation above sea-level (in meters);    -   IMPACT is the total observable impact from the sum of the        reports near the METAR station included in the historical        weather event impact data 114;    -   SNOW_EVENTS is the total number of days of snowfall in the        dataset;    -   MAGNITUDE is the ranking of the weather event, when sorted by        total daily snowfall (“SNOWFALL”);    -   N is the number of years in the dataset;    -   R is the number of years for a snowfall event to be exceeded at        least once for probability calculation;    -   T is (N+1)/MAGNITUDE, which is known as “a recurrence interval”;    -   P is 1/T, which is the probability of an event with the        recurrence interval, T;    -   PE is 1−(P**R), also known as “the probability of exceedance,”        described as the risk of failure (for example, 10 year snowfalls        have a 10% chance of occurrence in any given year, or PE=0.10);    -   W_IMPACT is IMPACT/POPULATION (the weighted observable impact);    -   P_IMPACT is the predicted observable impact as determined, for        example, by the regression algorithm determined in step 310;    -   IMPACT_VALUE is T*P_IMPACT;    -   IMPACT_INDICATOR is the result of a series of IF statements        fitting the distribution of the data into 10 categories scaled        from 1 to 10, where 10 is the highest IMPACT_VALUE and 1 is the        lowest IMPACT_VALUE.

Referring back to FIG. 3, the observable impact of each of the pastweather events are weighted by population in step 304 to account for thefact that weather events in less populated areas would have more impactin more populated areas. As shown in the examples in FIGS. 4 and 5, theweighted observable impact W_IMPACT is IMPACT/POPULATION.

Each weather event includes one or more relevant weather conditions. Awinter storm, for example, may include both snowfall (measured, forexample, in inches) and low temperatures (measured, for example, indegrees Fahrenheit). The historical weather data 112 includesinformation indicative those past weather conditions. For each of thepast weather events, the peril index analytics system 100 determines oneor more relevant weather conditions (e.g., snow, rain, ice, wind,temperature, etc.) in step 306.

The likelihood of each of the past weather conditions reoccurring atthat magnitude, known statistically as a return frequency, is determinedin step 308. As shown in the examples in FIGS. 4 and 5:

-   -   N is the number of years in the dataset;    -   R is the number of years for a snowfall event to be exceeded at        least once for probability calculation;    -   T is (N+1)/MAGNITUDE, which is known as “a recurrence interval”;    -   P is 1/T, which is the probability of an event with the        recurrence interval, T; and    -   PE is 1−(P**R), also known as “the probability of exceedance,”        described as the risk of failure (for example, 10 year snowfalls        have a 10% chance of occurrence in any given year, or PE=0.10).

For each of the relevant weather conditions, correlations are determinedbetween those the past weather conditions (as well as other explanatoryvariables) and the observable impact of those past weather conditions instep 310. For example, the peril index analytics system 100 may use aregression algorithm for W_IMPACT (dependent variable) using multipleregressors (independent variables) following the equationY=β ₀+β₁ X ₁+β₂ X ₂+ . . . +β_(k) X _(k), where

X_(k) are k number of predictor variables; and

β_(k) are regression coefficients.

An initial regression model for snowfall, for example, may determinethatP_IMPACT=β₀+β₁SNOWFALL

In order to determine the additional predictor variables (X₂, etc.) forsnowfall, residuals from the initial regression model may be plotted inArcGIS and, using a cluster analysis, locations may be grouped intodifferent regional groups. The regression algorithm would then beperformed on a number of groups with each group having a uniquecoefficient β_(k) for each potential predictor variable X_(k). Theaddition predictor variables may be PE (probability of exceedance),elevation, household size, demographic information, seasonality metrics,etc.

Accordingly, for each location of a past weather event, the peril indexanalytics system 100 determines formulas to predict the observableimpact of each weather condition (e.g., snow, rain, ice, wind,temperature, etc.), and additional features of that location. Notably,the coefficients (β₀, β₁, . . . , β_(k)) and the additional predictorvariables (X₂, . . . X_(k)) are separately determined for each weathercondition and location.

For each location of a past weather event, the peril index analyticssystem 100 calculates the predicted observable impact of each of thepast weather conditions in step 312. As shown in the examples in FIGS. 4and 5, the predicted observable impact (P_IMPACT) is calculated based onthe SNOWFALL amount and other predictor variables using the formuladetermined by the regression algorithm in step 310.

The peril index analytics system 100 is used to predict the impact offuture weather conditions, which is a function of both the observableimpact stored in the historical weather impact data 114 and how oftenthat location experiences those forecasted weather conditions. In otherwords, a weather event that occurs less often in a particular locationwill likely have more impact in that location than if those same weatherconditions were a frequent occurrence in that location.

Accordingly, the predicted impact of the past weather conditionsrecurring is determined in step 314. As shown in the examples in FIGS. 4and 5, predicted impact of the past weather events recurring(IMPACT_VALUE) is the recurrence interval (T) multiplied by thepredicted observable impact (P_IMPACT).

For each location of a past weather event, the past weather events aresorted by the predicted impact of the past weather events recurring instep 316. As shown in the examples in FIGS. 4 and 5, the past weatherevents are sorted by the predicted impact of the past weather eventsrecurring (IMPACT_VALUE).

For each location of a past weather event, the past weather events aregrouped based on the predicted impact of the past weather eventsrecurring (IMPACT_VALUE) in step 318. The past weather events are sortedinto groups. For example, the Jenks optimization method may be used toassign each of the past weather events to a number of groups (e.g., 10groups) based on the predicted impact of the past weather eventsrecurring (IMPACT_VALUE). Using the Jenks optimization method, alsocalled the Jenks natural breaks classification method, places each ofthe weather events into one of the groups so as to minimize each group'saverage deviation from the group's mean, while maximizing each group'sdeviation from the means of the other groups. In other words, the methodseeks to reduce the variance within groups and maximize the variancebetween groups. As shown in the example in FIG. 4, the five weatherevents with the highest IMPACT_VALUE are placed in IMPACT_INDICATORgroup 10, the weather event with the next highest IMPACT_VALUE is placedin IMPACT_INDICATOR group 9, etc.

For each location of a past weather event, thresholds are determined foreach group in step 320. In one embodiment, the threshold for each groupmay be the minimum amount of the weather condition in that group. Usingthe example in FIG. 4, the peril index system 100 may determine that thethreshold for group 10 is a weather event equal to 7.0 inches ofsnowfall (i.e., the lowest amount of snowfall in group 10). In anotherembodiment, the threshold for each group may be the minimum amount ofthe weather condition in the group below. Using the example in FIG. 4,the threshold for group 10 would be 6.7 inches of snowfall (i.e., thehighest amount of snowfall in group 9). In this embodiment, thethreshold for group 1 would be 0. In other embodiments, the thresholdfor each group may be between the minimum amount of the weathercondition in that group and the maximum amount of the weather conditionin the group below. Using the example in FIG. 4, the threshold for group10 would be between 7.0 inches and 6.7 inches of snowfall.

Steps 312 through 320 are performed for each weather condition in eachlocation of a past weather event. Accordingly, the peril index analyticssystem 100 determines a number of thresholds (e.g., 10 thresholds) foreach of a plurality of weather conditions (e.g., snow, rain, ice, wind,heat, cold) in each location of a past weather event.

The thresholds for the locations of the past weather events may beinterpolated for additional geographic locations in step 322. Asdescribed above with reference to step 302, each past weather event andthe impact of each past weather event may be spatially joined based onproximity to a number of discrete locations (e.g., the locations ofMETAR stations). In order to determine thresholds for additionalgeographic locations, the peril index analytics system 100 may use aKriging technique to interpolate the thresholds into a smooth rastersurface that includes thresholds for each geographic location in theentire coverage area of the peril index analytics system 100 (forexample, the continental United States and lower provinces in Canada).

Forecasted weather conditions are received in step 324. The forecastedweather conditions may be for any time period, from an hourly forecastto a seasonal forecast or even a yearly forecast. Accordingly, the perilindex analytics system 100 may be used to predict not only the impact ofa particular weather event, but the impact of all weather events thatare likely to impact each location over a long time period.

The predicted impact of the forecasted weather conditions is determinedin step 326. The forecasted weather conditions in each location arecompared to the thresholds for that location to determine the predictedimpact of those weather conditions. Using the example illustrated inFIG. 4, if a forecast includes 10 inches of snowfall in Alamosa, Colo.,then impact of that weather condition is classified as a 10 because theforecasted snowfall amount is greater than the threshold for group 10.The predicted impact of forecasted weather conditions is output via theGUI 190. The predicted impact of forecasted weather conditions may beoutput in a multitude of formats, including textural data, graphicalimages (GIS layers), etc. Other presentation formats, such as audio andvideo displays, may be utilized depending upon the specific use orapplication.

FIGS. 6-7 illustrates the predicted impact of a forecasted weathercondition as determined and output in graphical format according toexemplary embodiments of the present invention.

As shown in FIG. 6, the forecasted weather condition is rain forecastedfor during the time period of May 2017. The predicted impact of theforecasted rain in each location, which is determined by comparing themaximum forecasted rainfall amount during the time period to thethresholds (determined as described above) for rain in each of thelocations of the forecasted rainfall, is superimposed on a map as shown.In the example shown in FIG. 6, there are 10 thresholds for 10 groups.Group 10 represents the highest predicted impact, which represents anamount of rain that rarely occurs in that location (high recurrenceinterval) and has a large predicted observable impact (based on thecorrelations described above). Group 1-9 represent lower predictedimpacts, meaning amounts of rain that occur more often in that locationand/or have a lower predicted observable impact.

As shown in FIG. 7, the forecasted weather condition is rain forecastedfor during the time period of Summer 2017. The predicted impact of theforecasted rain in each location, which is determined by comparing themaximum forecasted rainfall amount during the time period to thethresholds (determined as described above) for rain in each of thelocations of the forecasted rainfall. In the example shown in FIG. 7,there are 10 thresholds for 10 groups. Group 10 represents the highestpredicted impact, which represents an amount of rain that rarely occursin that location (high recurrence interval) and has a large predictedobservable impact (based on the correlations described above). Group 1-9represent lower predicted impacts, meaning amounts of rain that occurmore often in that location and/or have a lower predicted observableimpact.

FIG. 8A shows an example of forecasted weather conditions, specificallysnowfall during the time period of Mar. 13, 2017. FIG. 8B illustratesthe predicted impact of the forecasted weather conditions shown in FIG.8A according to an exemplary embodiment of the present invention.

FIG. 9A shows another example of forecasted weather conditions, againsnowfall during the time period of Mar. 13, 2017. FIG. 9B illustratesthe predicted impact of the forecasted weather conditions shown in FIG.9A according to an exemplary embodiment of the present invention.

FIG. 10A shows another example of a forecasted weather conditions, againsnowfall during the time period of Mar. 13, 2017. FIG. 10B illustratesthe predicted impact of the forecasted weather conditions shown in FIG.10A according to an exemplary embodiment of the present invention.

FIG. 11A shows another example of a forecasted weather conditions, againsnowfall during the time period of Mar. 13, 2017. FIG. 11B illustratesthe predicted impact of the forecasted weather conditions shown in FIG.11A according to an exemplary embodiment of the present invention.

As shown in FIGS. 8A-B, 9A-B, 10A-B, and 11A-B, the predicted impact ofthe forecasted weather conditions is related not only to the magnitudeof those forecasted weather conditions, but also the characteristics ofthose particular locations, most notably the recurrence interval ofthose forecasted weather conditions at those magnitudes in thoselocations.

The peril index analytics system 100 may also be used to output alertsbased on the impact of forecasted weather conditions (as opposed tomerely the severity of those forecasted weather conditions). Forexample, the peril index analytics system 100 may also be used to outputalert to a user if the impact of a forecasted weather condition ispredicted to exceed one of the thresholds specified by the user. Thealerts may be output via the graphical user interface 190, email, SMS,smartphone notification, etc.

FIGS. 12-14 illustrate the locations of alerts that may be output basedon forecasted weather conditions (in this example, the forecastedweather conditions for Hurricane Matthew) according to exemplaryembodiments of the present invention.

The peril index analytics system 100 may also predict the impact offorecasted environmental events using a similar process as describedabove with respect to forecasted weather events.

FIG. 15 is a flow chart of a process 1500 for predicting the impact offorecasted environmental conditions.

Similar to the process 300, the process 1500 may be performed by theanalysis unit 180. Similar to step, 302, the observable impact of atleast some of the past environmental events is determined in step 1502.Similar to step 304, the observable impact of each of the pastenvironmental events may be weighted by population in step 1504. Similarto step 306, one or more relevant environmental conditions aredetermined in step 1506. Similar to step 308, the return frequency ofeach environmental condition is determined in step 1508. Similar to step310, for each of the relevant environmental conditions, correlations aredetermined between those the past environmental conditions and theobservable impact of those past environmental conditions in step 1510.Similar to step 312, the predicted observable impact of each of the pastenvironmental conditions is calculated in step 312. Similar to step 314,the predicted impact of the past environmental events recurring isdetermined in step 1514. Similar to step 316, the past environmentalevents are sorted by the risk of the environmental condition recurringin step 1516. Similar to step 318, the past environmental events aregrouped based on the predicted impact of the past weather eventsrecurring in step 1518. Similar to step 320, thresholds are determinedfor each group in step 1520. Similar to step 322, the thresholds for thelocations of the past environmental events may be interpolated foradditional geographic locations in step 1522. Similar to step 324,forecasted environmental conditions are received in step 1524. Similarto step 326, the predicted impact of the forecasted environmentalconditions is determined in step 1526.

As described above, the peril index analytics system 100 may output thepredicted impact of the forecasted environmental conditions and/oroutput alerts based on the predicted impact of the forecastedenvironmental conditions.

FIG. 16 is a flow chart of a process 1600 for predicting the impact offorecasted geologic conditions.

Similar to the process 300, the process 1600 may be performed by theanalysis unit 180. Similar to step, 302, the observable impact of atleast some of the past geologic events is determined in step 1602.Similar to step 304, the observable impact of each of the past geologicevents may be weighted by population in step 1604. Similar to step 306,one or more relevant geologic conditions are determined in step 1606.Similar to step 308, the return frequency of each geologic condition isdetermined in step 1608. Similar to step 310, for each of the relevantgeologic conditions, correlations are determined between those the pastgeologic conditions and the observable impact of those past geologicconditions in step 1610. Similar to step 312, the predicted observableimpact of each of the past geologic conditions is calculated in step312. Similar to step 314, the predicted impact of the past geologicevents recurring is determined in step 1614. Similar to step 316, thepast geologic events are sorted by the risk of the geologic conditionrecurring in step 1616. Similar to step 318, the past geologic eventsare grouped based on the predicted impact of the past weather eventsrecurring in step 1618. Similar to step 320, thresholds are determinedfor each group in step 1620. Similar to step 322, the thresholds for thelocations of the past geologic events may be interpolated for additionalgeographic locations in step 1622. Similar to step 324, forecastedgeologic conditions are received in step 1624. Similar to step 326, thepredicted impact of the forecasted geologic conditions is determined instep 1626.

As described above, the peril index analytics system 100 may output thepredicted impact of the forecasted geologic conditions and/or outputalerts based on the predicted impact of the forecasted geologicconditions.

While a preferred embodiment has been set forth above, those skilled inthe art who have reviewed the present disclosure will readily appreciatethat other embodiments can be realized within the scope of the presentinvention. Disclosures of specific technologies are also illustrativerather than limiting. Therefore, the present invention should beconstrued as limited only by the claims.

The invention claimed is:
 1. A method of graphically displayingpredicted financial impacts of environmental conditions over geographicregions, the method comprising: receiving, using control circuitry,information indicative of past environmental events in a plurality oflocations, each of the past environmental events including one or morepast environmental conditions; receiving, using the control circuitry,information indicative of the observable financial impact of at leastsome of the past environmental events; for each of the plurality oflocations and each of the one or more environmental conditions:determining, using the control circuitry, a recurrence interval of eachof the past environmental conditions in the plurality of locations;determining, using the control circuitry, correlations between the pastenvironmental conditions and the observable financial impact of the pastenvironmental events; calculating, using the control circuitry, apredicted observable financial impact of each of the past environmentalconditions; calculating, using the control circuitry, a predictedfinancial impact of each of the past environmental conditions recurringby multiplying the predicted observable financial impact the pastenvironmental event by the recurrence interval of the past environmentalcondition; grouping, using the control circuitry, the past environmentalevents into a plurality of groups based on the predicted financialimpact of the past environmental condition recurring; and determining,using the control circuitry, a threshold for each of the plurality ofgroups; receiving, using the control circuitry, current or forecastedenvironmental conditions; determining, using the control circuitry, thepredicted financial impact of the current or forecasted environmentalconditions by comparing the current or forecasted environmentalconditions with the thresholds determined for each of the groups in theplurality of locations of the current or forecasted environmentalconditions; and generating for display, in a graphical user interface, amap of the plurality of locations that graphically describes thepredicted financial impact of the current or forecasted environmentalconditions.
 2. The method of claim 1, wherein the correlation betweenthe past environmental condition and the observable financial impact ofthe past environmental event is determined using a regression algorithm.3. The method of claim 1, further comprising: weighting the observablefinancial impact of past environmental events by population.
 4. Themethod of claim 1, further comprising: interpolating the thresholds forthe plurality of locations to determine thresholds for additionallocations.
 5. The method of claim 4, wherein the thresholds areinterpolated using a Kriging technique.
 6. The method of claim 1,wherein determining thresholds for each of the one or more environmentalconditions comprises determining thresholds for each of a plurality ofenvironmental conditions and each of the plurality of locations.
 7. Themethod of claim 6, wherein the plurality of environmental conditionsinclude at least one of pollution, deforestation, depopulation,inhospitableness, biodegradable pollution, nonbiodegradable pollution,air pollution, noise pollution, sound pollution, thermal pollution, orwater pollution.
 8. A system for graphically displaying predictedfinancial impact of environmental conditions over geographic regions,comprising: one or more databases that store: information indicative ofpast environmental events in a plurality of locations, each of the pastenvironmental events including one or more past environmentalconditions; and information indicative of the observable financialimpact of at least some of the past environmental events; an analysisunit that, for each of the plurality of locations and each of the one ormore environmental conditions: determines a recurrence interval of eachof the past environmental conditions in each of the plurality oflocations; determines correlations between the past environmentalconditions and the observable financial impact of the past environmentalevents; calculates a predicted observable financial impact of each ofthe past environmental conditions; calculates a predicted financialimpact of each of the past environmental conditions recurring bymultiplying the predicted observable financial impact the pastenvironmental condition by the recurrence interval of the pastenvironmental condition; groups the past environmental events into aplurality of groups based on the predicted financial impact of the pastenvironmental condition recurring; determines a threshold for each ofthe plurality of groups; receives current or forecasted environmentalconditions; determines the predicted financial impact of the current orforecasted environmental conditions by comparing the current orforecasted environmental conditions with the thresholds determined foreach of the groups in the plurality of locations of the current orforecasted environmental events; and generates for display, in agraphical user interface, a map of the plurality of locations thatgraphically describes the predicted financial impact of the current orforecasted environmental conditions.
 9. The system of claim 8, whereinthe analysis unit is further configured to determine the correlationbetween the past environmental condition and the observable financialimpact of the past environmental event using a regression algorithm. 10.The system of claim 8, wherein the analysis unit is further configuredto weight the observable financial impact of past environmental eventsby population.
 11. The system of claim 8, wherein the analysis unit isfurther configured to interpolate the thresholds for the plurality oflocations to determine thresholds for additional locations.
 12. Thesystem of claim 11, wherein the analysis unit is further configured tointerpolate the thresholds using a Kriging technique.
 13. The system ofclaim 8, wherein the analysis unit is configured to determine thresholdsfor each of a plurality of environmental conditions in each of aplurality of locations.
 14. The system of claim 13, wherein theplurality of environmental conditions include at least one of pollution,deforestation, depopulation, inhospitableness, biodegradable pollution,nonbiodegradable pollution, air pollution, noise pollution, soundpollution, thermal pollution, or water pollution.
 15. A non-transitorycomputer-readable storage medium for graphically displaying predictedfinancial impacts of environmental conditions over geographic regionsstoring instructions that, when executed by a computer process, cause acomputing device to: receive information indicative of pastenvironmental events in a plurality of locations, each of the pastenvironmental events including one or more past environmentalconditions; receive information indicative of the observable financialimpact of at least some of the past environmental events; for each ofthe plurality of locations and each of the one or more environmentalconditions: determine a recurrence interval of each of the pastenvironmental conditions in each of the plurality of locations;determine correlations between the past environmental conditions and theobservable financial impact of the past environmental events; calculatea predicted observable financial impact of each of the pastenvironmental conditions; calculate a predicted financial impact of eachof the past environmental conditions recurring by multiplying thepredicted observable financial impact the past environmental conditionby the recurrence interval of the past environmental condition; groupthe past environmental events into a plurality of groups based on thepredicted financial impact of the past environmental conditionrecurring; and determine threshold for each of the plurality of groups;receive current or forecasted environmental conditions; determine thepredicted financial impact of the current or forecasted environmentalconditions by comparing the current or forecasted environmentalconditions with the thresholds determined for each of the groups in theplurality of locations of the current or forecasted environmentalevents; and generating for display, in a graphical user interface, a mapof the plurality of locations that graphically describes the predictedfinancial impact of the current or forecasted environmental conditions.16. A method of graphically displaying predicted financial impacts ofgeologic conditions over geographic regions, the method comprising:receiving, using control circuitry, information indicative of pastgeologic events in a plurality of locations, each of the past geologicevents including one or more past geologic conditions; receiving, usingthe control circuitry, information indicative of the observablefinancial impact of at least some of the past geologic events; for eachof the plurality of locations and each of the one or more geologicconditions: determining, using the control circuitry, a recurrenceinterval of each of the past geologic conditions in each of theplurality of locations; determining, using the control circuitry,correlations between the past geologic conditions and the observablefinancial impact of the past geologic events; calculating, using thecontrol circuitry, a predicted observable financial impact of each ofthe past geologic conditions; calculating, using the control circuitry,a predicted financial impact of each of the past geologic conditionsrecurring by multiplying the predicted observable financial impact thepast geologic condition by the recurrence interval of the past geologiccondition; grouping, using the control circuitry, the past geologicevents into a plurality of groups based on the predicted financialimpact of the past geologic condition recurring; and determining, usingthe control circuitry, a threshold for each of the plurality of groups;receiving, using the control circuitry, current or forecasted geologicconditions; determining, using the control circuitry, the predictedfinancial impact of the current or forecasted geologic conditions bycomparing the current or forecasted geologic conditions with thethresholds determined for each of the groups in the plurality oflocations of the current or forecasted geologic events; and generatingfor display, in a graphical user interface, a map of the plurality oflocations that graphically describes the predicted financial impact ofthe current or forecasted geologic conditions.
 17. The method of claim16, wherein the correlation between the past geologic condition and theobservable financial impact of the past geologic event is determinedusing regression algorithm.
 18. The method of claim 16, furthercomprising: weighting the observable financial impact of past geologicevents by population.
 19. The method of claim 16, further comprising:interpolating the thresholds for the plurality of locations to determinethresholds for additional locations.
 20. The method of claim 19, whereinthe thresholds are interpolated using a Kriging technique.
 21. Themethod of claim 16, wherein determining thresholds for each of the oneor more geologic conditions comprises determining thresholds for each ofa plurality of geologic conditions and each of the plurality oflocations.
 22. The method of claim 21, wherein the plurality of geologicconditions include at least one of erosion, glaciation, volcaniceruption or emission, earthquake, tsunami, avalanche, landslide, ormudslide.
 23. A system for graphically displaying predicted financialimpacts of geologic conditions over geographic regions, comprising: oneor more databases that store: information indicative of past geologicevents in a plurality of locations, each of the past geologic eventsincluding one or more past geologic conditions; and informationindicative of the observable financial impact of at least some of thepast geologic events; an analysis unit that, for each of the pluralityof locations and each of the one or more geologic conditions: determinesa recurrence interval of each of the past environmental conditions ineach of the plurality of locations; determines correlations between thepast geologic conditions and the observable financial impact of the pastgeologic events; calculates a predicted observable financial impact ofeach of the past geologic conditions; calculates a predicted financialimpact of each of the past geologic conditions recurring by multiplyingthe predicted observable financial impact the past geologic condition bythe recurrence interval of the past geologic condition; groups the pastgeologic events into a plurality of groups based on the predictedfinancial impact of the past geologic condition recurring; determines athreshold for each of the plurality of groups; receives current orforecasted geologic conditions; determines the predicted financialimpact of the current or forecasted geologic conditions by comparing thecurrent or forecasted geologic conditions with the thresholds determinedfor each of the groups in the plurality of locations of the current orforecasted geologic events; and generating for display, in a graphicaluser interface, a map of the plurality of locations that graphicallydescribes the predicted financial impact of the current or forecastedgeologic conditions.
 24. The system of claim 23, wherein the analysisunit is further configured to determine the correlation between the pastgeologic condition and the observable financial impact of the pastgeologic event using a regression algorithm.
 25. The system of claim 23,wherein the analysis unit is further configured to weight the observablefinancial impact of past geologic events by population.
 26. The systemof claim 23, wherein the analysis unit is further configured tointerpolate the thresholds for the plurality of locations to determinethresholds for additional locations.
 27. The system of claim 26, whereinthe analysis unit is further configured to interpolate the thresholdsusing a Kriging technique.
 28. The system of claim 23, wherein theanalysis unit is configured to determine thresholds for each of aplurality of geologic conditions in each of a plurality of locations.29. The system of claim 28, wherein the plurality of geologic conditionsinclude at least one of erosion, glaciation, volcanic eruption oremission, earthquake, tsunami, avalanche, landslide, or mudslide.
 30. Anon-transitory computer-readable storage medium for graphicallydisplaying predicted financial impacts of geologic conditions overgeographic regions storing instructions that, when executed by acomputer process, cause a computing device to: receive informationindicative of past geologic events in a plurality of locations, each ofthe past geologic events including one or more past geologic conditions;receive information indicative of the observable financial impact of atleast some of the past geologic events; for each of the plurality oflocations and each of the one or more geologic conditions: determine arecurrence interval of each of the past geologic conditions in each ofthe plurality of locations; determine correlation between the pastgeologic conditions and the observable financial impact of the pastgeologic events; calculate a predicted observable financial impact ofeach of the past geologic conditions; calculate a predicted financialimpact of each of the past geologic conditions recurring by multiplyingthe predicted observable financial impact the past geologic condition bythe recurrence interval of the past geologic condition; group the pastgeologic events into a plurality of groups based on the predictedfinancial impact of the past geologic condition recurring; and determinethreshold for each of the plurality of groups; receive current orforecasted geologic conditions; and determine the predicted financialimpact of the current or forecasted geologic conditions by comparing thecurrent or forecasted geologic conditions with the thresholds determinedfor each of the groups in the plurality of locations of the current orforecasted geologic events; and generate for display, in a graphicaluser interface, a map of the plurality of locations that graphicallydescribes the predicted financial impact of the current or forecastedgeologic conditions.